Multiple Condition Coverage Testing

Modbat [34] is an open-source tool based on extended finite-state machines specialized for testing the APIs of software. A Scala-based domain-specific language is used to create the models with features for probabilistic and nondeterministic transitions, component models with inheritance, and exceptions. Test cases are generated as sequences of method calls to the API that can be directly executed against the SUT. HTG uses a hybrid automaton model or SPICE netlists [28] as input and generates test cases in C++. A data coverage measure based on star discrepancy [29] is used to guide the test generation and ensure the test cases are relatively equally distributed over the possible data space. The generated test cases can be applied to numeric simulation and circuit simulation domains.

Let us understand how change impact analysis works and how it helps you find the right testing direction with TestCompass. Here, we sketch the ideas of two approaches that are focused on improving the test generation process and the test quality, respectively. Finally, we increment the classical usage of the CertifyIt tool for functional and security testing and integrate it into a MBT as a Service (MBTAAS) environment, which delivers immense value for the IoT community. The adaptation of CertifyIt for the IoT domain has already shown its value, as discussed by the authors in [3] and [45]. Compared with FCCH, EC-SCH, and EC-BCCH that have been described in Section 3.2.6, the EC-CCCH/D channel makes use of CCs introduced in Section 3.2.8, to be able to reach users in different coverage conditions effectively.

Decision or branch coverage is a measure of branches that have been evaluated to both true and false in testing. When branches contain multiple conditions, branch coverage can be 100% without instantiating all conditions to true/false. The EC-PDTCH/U peak physical layer https://www.globalcloudteam.com/ data rate matches the EC-PDTCH/D 489.6 kbps across the 20 ms TTI. For devices only supporting GMSK modulation on the transmitter side, the highest modulation and coding scheme is MCS-4, which contains a RLC/MAC header of 4 octets and a single RLC block of 44 octets.

In this work the transmission of one MBytes of firmware data is required in presence of normal application traffic. The performance using unicast transmission is compared to the one using Single Cell Point-to-Multipoint (SC-PTM), a feature introduced in Rel-14 of NB-IoT standard to enable multicast communication. The gains in terms of delivery time introduced by SC-PTM are quite obvious w.r.t. unicast. For unicast mode the delivery time varies from the order of hours to 1 day when increasing the ISD from 500 m to 1732 m, while it varies from the order of minutes to 1 hour for the SC-PTM. This indicates that the effective gains of SC-PTM w.r.t. unicast mode are strictly related to the location of UE. Nevertheless, it is worth emphasizing that while the delivery time is affected by the number of UE in the unicast case, the SC-PTM has a performance that does not vary with the number of UE being served.

The NB-IoT small cell could transmit up to 15 dBm for adjacent channel with a 1 MHz guard band and there is no angular antenna discrimination. If it is considered, the improvement in the EIRP will be equivalent to the antenna discrimination (16 dB). The maximum allowable EIRP that the IoT devices can transmit varies between 9 and 14 dBm for the best case (Smart Parking) with a 2 MHz guard band. For the most restrictive case (Traffic Congestion) it is possible to transmit between 3 and 8 dBm with the same guard band, being this power enough to ensure a right operation. To efficiently perform the tests, VERA also provides a library containing common vulnerability test patterns for modeling. In this section, we present a representative set of academic and industrial MBT tools for test case generation that we investigated for usage in the IoT domain and discuss their characteristics.

What is Multiple Condition Coverage?

Line coverage reports on the execution footprint of testing in terms of which lines of code were executed to complete the test. Edge coverage reports which branches or code decision points were executed to complete the test. The meaning of this depends on what form(s) of coverage have been used, as 67% branch coverage is more comprehensive than 67% statement coverage. Structural testing approaches are often referred to as white box or glass box. They are also sometimes referred to as Adequacy Criteria which reflects the perception of test data in terms of structural coverage criteria. For example, for a given program, if a set of test data exercises all statements then the set is described as being statement testing, or statement coverage, adequate.

You need to verify that using this

coverage type does not modify the behavior of the software. Condition coverage is also known as Predicate Coverage in which each one of the Boolean expression have been evaluated to both TRUE and FALSE. Each testing project has some kind of test management tools (from simple Microsoft Excel lists up to advanced tools like HP Quality Center). In such tools, test cases are managed and their lifecycle (create, execute, evaluate) is stored. Since MBT is all about generating test cases and their number can be very high, the direct interface to test management tools is strongly needed.

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To mitigate this problem the Modified Condition/Decision Coverage metric was created. This website is using a security service to protect itself from online attacks. There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data.

This can be done, e.g., by increasing a variable every time the control flow gets closer to the place where the original variable is set to the desired value. With this technique, they were able to improve evolutionary test generation considerably. Tcases [38] is a combinatorial testing tool to test system functions and generate input testing data. An XML document defining the SUT as a set of functions is required as input as well as the data space for variables of the functions. The test generation is guided by a predefined data coverage level, through which the number of generated test cases can be controlled. The test cases are in stored XML and can be transformed to JUnit test cases via an integrated convector.

Tables 4.4 and 4.5 summarizes the findings of this and the previous section in terms of MAC-layer data rates supported at 164 dB MCL and the peak physical layer data rates experienced under error-free conditions. In addition, it presents the MAC-layer data rates simulated at coupling losses of 154 and 144 dB. For the 33 dBm device MCS-1 is providing the best performance at 164 and 154 dB coupling loss. At 144 dB coupling loss MCS-3 is the best choice in the uplink even when 8PSK is supported, while MCS-4 provides the highest data rate for the downlink.

The later are created during requirements engineering in special tools (for example DOORS10). To support features like traceability, comprehensibility, coverage measurement, etc. this interface has to be given. Further, the modeling task in industrial projects is mostly done in separate modeling tools (as for example Sparx Systems Enterprise Architect11) and not by using the modeling feature of MBT tools. Surveys on quality assurance show that most of the errors in software are introduced during requirements elicitation within the development process [35].

The number of enumerated branches is the number of

distinct available cases for each composition of logical operators

(standard or derived, but not overloaded). Two branches are enumerated for each boolean basic

condition, and one per case basic condition. In order to ensure complete Condition coverage criteria for the above example, A, B and C should be evaluated at least once against “true” and “false”. However, this set of tests does not satisfy branch coverage since neither case will meet the if condition.

The maximum supported EC-GSM-IoT RLC window size of 16 limits the number of RLC blocks that at any given time can be outstanding with a pending acknowledgment status. The base station uses the RRBP field in the RLC header of the EC-PDTCH/D block to poll the device for a Packet Downlink Ack/Nack (PDAN) report. The device responds earliest 40 ms after the end of the EC-PDTCH/D transmission time interval (TTI) as illustrated in Fig. A forced condition is a multiple condition in which

any occurrence of the or else operator is replaced with the or

operator, and the and then operator is replaced with the and

• In a few cases, obtaining complete MCDC coverage required construction of code stubs to force a particular sequence of tests, with specific combinations, to be executed.
• It provides the necessary interfaces to test a wide range of objects from individual C++ classes to GUI applications and distributed systems containing different devices.
• Variables are categorised as being defined (assigned some value) or used (referenced in some way).
• In practice a large number of mutants are created automatically, each containing one fault.
• A set of coverage criteria, including decision coverage and condition coverage, are used to guide the generation process.
• The proposed scenario considers a DVB-T2 network offering fixed rooftop reception as a primary service and NB-IoT network as a secondary service allocated to DTT white spaces.

operator.

In practice a large number of mutants are created automatically, each containing one fault. Condition coverage testing is a type of white-box testing that tests all the conditional expressions in a program for all possible outcomes of the conditions. Finally, not only test cases for manual, but also test scripts for automatic execution are generated in MBT. The automatic execution is done by test automation tools with their own syntax and semantic.

Most of the diagrams have a simple syntax and fairly clear semantics such that customer and developer can easily learn how to express their requirements more precisely, thus enabling the principle close collaboration. The changes in requirements can easily be made on the already created models, thus improving fast adaptation. Models can also support the conversation between team members, where the results of a discussion can be edited into the models immediately. Also the simplicity principle can be supported by models by using the abstraction, modularization, and decomposition features of modeling. Another challenge of the state-of-the-art MBT is the missing support for non-functional testing. As stated by the survey from Dias Neto et al. [18], most of the MBT approaches concentrate on functional testing, coverage criteria, selection algorithms and the like.